Create a VoyageAI inference endpoint | Elasticsearch Serverless API documentation

Create a VoyageAI inference endpoint Generally available

PUT /_inference/{task_type}/{voyageai_inference_id}

Api key auth

Create an inference endpoint to perform an inference task with the voyageai service.

Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

The type of the inference task that the model will perform.

Values are text_embedding or rerank.
voyageai_inference_id string Required

The unique identifier of the inference endpoint.

application/json

Body

chunking_settings object

Chunking configuration object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
- overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
- sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
- strategy string
  
  The chunking strategy: sentence or word.
  
  Default value is sentence.
service string Required

Value is voyageai.
service_settings object Required
Hide service_settings attributes Show service_settings attributes object
- dimensions number
  
  The number of dimensions for resulting output embeddings. This setting maps to output_dimension in the VoyageAI documentation. Only for the text_embedding task type.
  
  External documentation
- model_id string Required
  
  The name of the model to use for the inference task. Refer to the VoyageAI documentation for the list of available text embedding and rerank models.
  
  External documentation
- rate_limit object
  
  This setting helps to minimize the number of rate limit errors returned from the service.
  Hide rate_limit attribute Show rate_limit attribute object
  
  requests_per_minute number
  
  The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:
  
  alibabacloud-ai-search service: 1000
  
  anthropic service: 50
  
  azureaistudio service: 240
  
  azureopenai service and task type text_embedding: 1440
  
  azureopenai service and task type completion: 120
  
  cohere service: 10000
  
  elastic service and task type chat_completion: 240
  
  googleaistudio service: 360
  
  googlevertexai service: 30000
  
  hugging_face service: 3000
  
  jinaai service: 2000
  
  mistral service: 240
  
  openai service and task type text_embedding: 3000
  
  openai service and task type completion: 500
  
  voyageai service: 2000
  
  watsonxai service: 120
- embedding_type number
  
  The data type for the embeddings to be returned. This setting maps to output_dtype in the VoyageAI documentation. Permitted values: float, int8, bit. int8 is a synonym of byte in the VoyageAI documentation. bit is a synonym of binary in the VoyageAI documentation. Only for the text_embedding task type.
  
  External documentation
task_settings object
Hide task_settings attributes Show task_settings attributes object
- input_type string
  
  Type of the input text. Permitted values: ingest (maps to document in the VoyageAI documentation), search (maps to query in the VoyageAI documentation). Only for the text_embedding task type.
- return_documents boolean
  
  Whether to return the source documents in the response. Only for the rerank task type.
  
  Default value is false.
- top_k number
  
  The number of most relevant documents to return. If not specified, the reranking results of all documents will be returned. Only for the rerank task type.
- truncation boolean
  
  Whether to truncate the input texts to fit within the context length.
  
  Default value is true.

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
  
  overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
  
  sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
  
  strategy string
  
  The chunking strategy: sentence or word.
  
  Default value is sentence.
- service string Required
  
  The service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Values are text_embedding or rerank.

PUT /_inference/{task_type}/{voyageai_inference_id}

PUT _inference/text_embedding/openai-embeddings { "service": "voyageai", "service_settings": { "model_id": "voyage-3-large", "dimensions": 512 } }

resp = client.inference.put( task_type="text_embedding", inference_id="openai-embeddings", inference_config={ "service": "voyageai", "service_settings": { "model_id": "voyage-3-large", "dimensions": 512 } }, )

const response = await client.inference.put({ task_type: "text_embedding", inference_id: "openai-embeddings", inference_config: { service: "voyageai", service_settings: { model_id: "voyage-3-large", dimensions: 512, }, }, });

response = client.inference.put( task_type: "text_embedding", inference_id: "openai-embeddings", body: { "service": "voyageai", "service_settings": { "model_id": "voyage-3-large", "dimensions": 512 } } )

$resp = $client->inference()->put([ "task_type" => "text_embedding", "inference_id" => "openai-embeddings", "body" => [ "service" => "voyageai", "service_settings" => [ "model_id" => "voyage-3-large", "dimensions" => 512, ], ], ]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"voyageai","service_settings":{"model_id":"voyage-3-large","dimensions":512}}' "$ELASTICSEARCH_URL/_inference/text_embedding/openai-embeddings"

client.inference().put(p -> p .inferenceId("openai-embeddings") .taskType(TaskType.TextEmbedding) .inferenceConfig(i -> i .service("voyageai") .serviceSettings(JsonData.fromJson("{\"model_id\":\"voyage-3-large\",\"dimensions\":512}")) ) );

Request examples

Run `PUT _inference/text_embedding/voyageai-embeddings` to create an inference endpoint that performs a `text_embedding` task. The embeddings created by requests to this endpoint will have 512 dimensions.

{ "service": "voyageai", "service_settings": { "model_id": "voyage-3-large", "dimensions": 512 } }

Run `PUT _inference/rerank/voyageai-rerank` to create an inference endpoint that performs a `rerank` task.

{ "service": "voyageai", "service_settings": { "model_id": "rerank-2" } }